Many modern vehicles include built-in advanced driver assistance systems (ADAS) to provide automated safety and/or assisted driving functionality. For example, these advanced driver assistance systems can implement adaptive cruise control, automatic parking, automated braking, blind spot monitoring, collision avoidance, driver drowsiness detection, lane departure warning, or the like. The next generation of vehicles can include autonomous driving (AD) systems to control and navigate the vehicles independent of human interaction.
These vehicles typically include multiple sensors, such as one or more cameras, a Light Detection and Ranging (Lidar) sensor, a Radio Detection and Ranging (Radar) system, or the like, to measure different portions of the environment around the vehicles. Each sensor processes their own measurements captured over time to detect an object within their field of view, and then provide a list of detected objects to the advanced driver assistance systems or the autonomous driving systems for their use in implementing automated safety and/or driving functionality. In some instances, the sensors can also provide a confidence level corresponding to their detection of objects on the list based on their captured measurements.
The advanced driver assistance systems or the autonomous driving systems can utilize the list of objects and, in some cases, the associated confidence levels of their detection, to implement automated safety and/or driving functionality. For example, when a radar sensor in the front of a vehicle provides the advanced driver assistance system in the vehicle a list having an object in a current path of the vehicle, the advanced driver assistance system can provide a warning to the driver of the vehicle or control vehicle in order to avoid a collision with the object.
Because some of the sensors can have at least partially overlapping fields of view, the advanced driver assistance systems or the autonomous driving systems can integrate the object lists in an attempt to confirm that an object detected by one sensor was also detected by another sensor. This integration of objects is sometimes referred to as object-level integration. When multiple sensors have detected the same object, the advanced driver assistance systems can increase the confidence level associated with the presence of the object. If, however, the sensors diverge—with a sensor detecting an object and another not detecting the object—the advanced driver assistance systems or the autonomous driving systems have to make a decision about how to react. For example, the advanced driver assistance systems or the autonomous driving systems can assume the presence of the object based on the object list from a single sensor, but with a lower the confidence level, ignore the detection of the object by the sensor, or delay making a decision to see if the sensors alter their object lists over time. Further, since each sensor performs its object detection separately based exclusively on its own captured measurements, as an object moves relative to the vehicle, it may leave a field of view of one sensor and have to be re-detected after entering into a field of view of a different sensor.